For more than 2,500 years, surgical teaching has been based on the so called "see one, do one, teach one" paradigm, in which the surgical trainee learns by operating on patients under close supervision of peers and superiors. However, higher demands on the quality of patient care and rising malpract...
For more than 2,500 years, surgical teaching has been based on the so called "see one, do one, teach one" paradigm, in which the surgical trainee learns by operating on patients under close supervision of peers and superiors. However, higher demands on the quality of patient care and rising malpractice costs have made it increasingly risky to train on patients. Minimally invasive surgery, in particular, has made it more difficult for an instructor to demonstrate the required manual skills. It has been recognized that, similar to flight simulators for pilots, virtual reality (VR) based surgical simulators promise a safer and more comprehensive way to train manual skills of medical personnel in general and surgeons in particular. One of the major challenges in the development of VR-based surgical trainers is the real-time and realistic simulation of interactions between surgical instruments and biological tissues. It involves multi-disciplinary research areas including soft tissue mechanical behavior, tool-tissue contact mechanics, computer haptics, computer graphics and robotics integrated into VR-based training systems. The research described in this paper addresses the problem of characterizing soft tissue properties for medical virtual environments. A system to measure in vivo mechanical properties of soft tissues was designed, and eleven sets of animal experiments were performed to measure in vivo and in vitro biomechanical properties of porcine intra-abdominal organs. Viscoelastic tissue parameters were then extracted by matching finite element model predictions with the empirical data. Finally, the tissue parameters were combined with geometric organ models segmented from the Visible Human Dataset and integrated into a minimally invasive surgical simulation system consisting of haptic interface devices and a graphic display.
For more than 2,500 years, surgical teaching has been based on the so called "see one, do one, teach one" paradigm, in which the surgical trainee learns by operating on patients under close supervision of peers and superiors. However, higher demands on the quality of patient care and rising malpractice costs have made it increasingly risky to train on patients. Minimally invasive surgery, in particular, has made it more difficult for an instructor to demonstrate the required manual skills. It has been recognized that, similar to flight simulators for pilots, virtual reality (VR) based surgical simulators promise a safer and more comprehensive way to train manual skills of medical personnel in general and surgeons in particular. One of the major challenges in the development of VR-based surgical trainers is the real-time and realistic simulation of interactions between surgical instruments and biological tissues. It involves multi-disciplinary research areas including soft tissue mechanical behavior, tool-tissue contact mechanics, computer haptics, computer graphics and robotics integrated into VR-based training systems. The research described in this paper addresses the problem of characterizing soft tissue properties for medical virtual environments. A system to measure in vivo mechanical properties of soft tissues was designed, and eleven sets of animal experiments were performed to measure in vivo and in vitro biomechanical properties of porcine intra-abdominal organs. Viscoelastic tissue parameters were then extracted by matching finite element model predictions with the empirical data. Finally, the tissue parameters were combined with geometric organ models segmented from the Visible Human Dataset and integrated into a minimally invasive surgical simulation system consisting of haptic interface devices and a graphic display.
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문제 정의
is the material parameter containing the viscoelasticity and nonlinear elasticity. The goal of this characterization is to determine these parameters for a proposed material law by minimizing the errors between the simulated and the associated experimental measurements. This process is also known as the inverse calculation, because it is the opposite of an ordinary simulation (that is, solving for forces or displacements given material parameters and boundary conditions).
가설 설정
Fig. 3 Force responses of the FE simulation and the experiments: a) liver with a 5mm indentation, b) kidney with a 6mm indentation. The data from the experiment were filtered out to remove noisy properties using a 3rd order Butterworth filter.
제안 방법
The viscoelastic and hyperelastic material parameters were estimated in two stages in the framework of the QLV. To calibrate the parameters to the experimental results, a three-dimensional FE model was developed to simulate the forces at the indenter, and an optimization program was also developed that updates new parameters and runs the simulation iteratively. Key assumptions in this approach are that the organs are incompressible, homogenous and isotropic; and that the deformations imposed are small compared to the organ’s size.
대상 데이터
Data were used from experiments conducted on intra-abdominal organs of pigs at the Harvard Center for Minimally Invasive Surgery, in collaboration with surgeons from the Massachusetts General Hospital (MGH) [6]. Ten pigs were used in these experiments and detailed information regarding the experiments, including that related to the instrumentation, protocol and data, can be found in a paper written by the same authors.
이론/모형
This method estimates unknown material parameters for a selected material law by minimizing the least-squares difference between predictions of a finite element model and experimental responses. This study uses the inverse finite element estimation method is used. This implementation combines a three dimensional finite element model with a nonlinear optimization algorithm to estimate material parameters by matching experimental results.
Given that a linear elastic material law cannot model these nonlinearities, a more general material law should be used to describe this behavior. The quasi-linear viscoelasticity (QLV) framework proposed by Fung[3] was used for modeling. This approach assumes that material behavior can be decoupled into two effects: a time-independent elastic response and a linear viscoelastic stress-relaxation response.
and m are measured forces, simulated forces, time and the total number of data points, respectively. Among several optimization algorithms that could be used, the nonlinear least square optimization known as the MarquardtLevenberg algorithm is adopted. It updates the parameters iteratively depending on the norm of JT J and the Marquardt parameter λ.
Two force profiles measured from the liver and kidney used in the experiments were selected. The viscoelastic parameters were estimated from these normalized profiles and were input into the ABAQUS database for viscoelastic modeling. Because a rough but reasonable initial guess was required for the organ’s elastic parameters, their static force responses were used.
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